The present state of the art in a supply chain is that often the first time that a problem is detected in when the manufactured item is delivered, and it is determined to be of insufficient quality, or it is not delivered when it was promised.
The present state of the art does not solve certain problems that need to be solved. Detection of unusual conditions is either performed manually, or it requires significant inter-application integration; if the applications reside in different enterprises (where someone in a customer company desires to know something happening in the manufacturing supplier) additional inter-enterprise, inter-application problems arise, not the least of which involves security of information access and information transfer.
What would really be useful is a device or process that is able to detect exception conditions in a manufacturing supply chain, with a process that is minimally invasive to the existing applications and devices that comprise the supply chain, and then inform one or more recipients, determined by the specific condition detected, instantly, and with each notification provide the users with relevant context to allow them to react to the original exception, either to fix the problem, or to provide an alternative solution to the problem, such as locating an alternative manufacturing supplier. The notification may take place wholly within an enterprise, or might cross enterprise boundaries; in the latter case, it is important that only information relevant to the pair of enterprises (e.g., customer and supplier) be allowed to be transmitted across the network connecting the enterprises. Also, it would be useful if the communications can occur via a variety of media; that there would be an escalation mechanism if notification is not acknowledged within a pre-determined length of time; and that there would be a mechanism to allow an exception condition to be reset, so that further occurrences of the condition be permitted to cause new notifications to be processed.
The process exception detection means of the present invention act as catalysts to regulate the processes that they monitor. Using distributed process and resource information, the event information is combined with the specific process for resolution, which is instantly delivered to responsible recipients anywhere. This enables the process exception detection means to initiate, within minutes, a “Red Flag” to the local and global business process owners when an exception occurs at a critical point in the supply chain. Organizations and the value networks in which they operate have the ability to respond proactively to both real-time exceptions as well as market trends through continuous process innovation.
Valuable quality and optimization tools are limited in their effectiveness by timely access to key data, both internally and at supplier locations. Organizations tend to rely on anecdotal data for key modeling metrics and optimization algorithms, because process data is either not available or due to latency, synchronizing or input accuracy not suitable for modeling or operational reliance.
Unplanned events in the supply chain and in particular unpredictable acts of nature, economic, or political changes have tremendous impact on modern time-critical value chains. The overwhelming availability of non-contextual information and data, through an array of proprietary applications, databases, portals and spreadsheets measurably affects knowledge workers.
Recognition of Distributed Processes
Unlike other traditional proprietary or point solutions that focus on event management of limited data sources, the process exception detection means of the present invention is designed for flexible monitoring, not control, of a broad range of source data—including but not limited to production equipment, environmental sensors, metrology/quality equipment, facilities sensors, applications, databases, and news feeds—independent of local infrastructures including communication and information systems. This provides the ability to monitor particular parameters relating to specific transactional as well as qualitative measures that are both intrinsic and extrinsic to the local and global processes. Some examples of the distributed processes in a supply chain environment include:
1. manufacturing process
2. management processes such as, marketing, logistics, accounting/tax, personnel, design/engineering, regulatory, and quality
3. environmental variables that impact quality
4. and the ability to create and “audit trail” of exceptions, communications and corrective actions across boundaries such as the corporate entity, language as well as technical.
Currently companies are limited by the ability of proprietary and other specific scope solutions allowing them to monitor and put into context certain limited aspects of the above, but not all simultaneously—and that we can monitor any of these, or any combination of these
Supply chain Statistical Process Control (SPC) barriers prevent timely response and effective decision making in a proactive, as opposed to re-active, mode and lack adequate information. These barriers to decision making can be translated in terms of the need to deliver real-time information as opposed to data. Many process improvement initiatives are stalled or companies are unable to assess the true impact of these initiatives due to the lack of some key supply chain analytics required to measure operational performance and ongoing return on investment. The present invention builds on familiar continuous improvement concepts to identify and address shortcomings; for example Six Sigma advocates the measurement and monitoring of critical processes. The barrier has been the inability to identify critical processes in the supply chain and to monitor processes beyond the boundaries of the enterprise and the inability to relate measures to meaningful decision metrics, and ancillary information, that can be used to trigger remedial action. The process exception detection means of the present invention provides the ability to monitor and control metrics that pose an operational challenge to measure. The ability of the process exception detection means to identify key performance levers of inter-enterprise processes and provide that source information in the context of distributed processes forms the basis of a supply chain statistical process control and continuous improvement system.
Choice of the pre-specified situations, for which the process exception detection means should be programmed to react, will typically be ones for which if action is delayed (for example, until information about the situation becomes known via other means) significant negative impact will accrue. Examples would be responding to supply chain exceptions to avoid huge financial impacts, or responding to natural, or other, disasters affecting a manufacturing location allowing alternative arrangements to be made by customers.
In general, systems commonly produce ‘events’—that is, when something that might or might not be interesting occurs. However, the number of these events can be huge—and it would be impossible for humans to be informed about each and every one of these events. Indeed, knowing about each of these events would not yield any useful information—it is just so much data. In contrast, there may be a few conditions (which might be as a result of specific events, but do not have to be) that are unusual, and would benefit from human involvement. Some of these conditions, if left undetected, or unreported, may have a significant financial impact on an organization. Thus, these conditions may be thought of as ‘exceptions’ (rather than ‘events’). In addition, for a human to be able to react to such an exception requires that they have the appropriate context for being able to interpret the exception (that has occurred) and thence be able to determine an appropriate action to take as a result—this requires having some contextual information (such as a process document) to help them understand the exception in context. The process exception detection means of the present invention allows detection of specific exceptions, and reporting those exceptions to humans with the necessary context to allow them to take appropriate action. Many events can be associated with gradual more insidious changes in other variables. The present invention allows users to define exception conditions measure them identify correlations and operationally monitor theses exceptions as to predict or identify critical risk factors.